It is widely accepted that the brain, like any other physical system, is subjected to physical constraints that restrict its operation. The brain's metabolic demands are particularly critical for proper neuronal function, but the impact of these constraints continues to remain poorly understood. Detailed single-neuron models are recently integrating metabolic constraints, but these models’ computational resources make it challenging to explore the dynamics of extended neural networks, which are governed by such constraints. Thus, there is a need for a simplified neuron model that incorporates metabolic activity and allows us to explore the dynamics of neural networks. This work introduces an energy-dependent leaky integrate-and-fire (EDLIF) neuronal model extension to account for the effects of metabolic constraints on the single-neuron behavior. This simple, energy-dependent model could describe the relationship between the average firing rate and the Adenosine triphosphate (ATP) cost as well as replicate a neuron's behavior under a clinical setting such as amyotrophic lateral sclerosis (ALS). Additionally, EDLIF model showed better performance in predicting real spike trains – in the sense of spike coincidence measure – than the classical leaky integrate-and-fire (LIF) model. The simplicity of the energy-dependent model presented here makes it computationally efficient and, thus, suitable for studying the dynamics of large neural networks.

中文翻译：

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扩展集成和激发模型以解释代谢依赖性

人们普遍认为，大脑与任何其他物理系统一样，受到限制其操作的物理约束。大脑的代谢需求对于正常的神经元功能尤其重要，但这些限制的影响仍然知之甚少。详细的单神经元模型最近正在整合代谢约束，但这些模型的计算资源使得探索受此类约束控制的扩展神经网络的动力学具有挑战性。因此，需要一种简化的神经元模型，该模型包含代谢活动并允许我们探索神经网络的动力学。这项工作引入了一个依赖能量的泄漏集成和发射 (EDLIF) 神经元模型扩展，以解释代谢约束对单神经元行为的影响。这个简单的、依赖能量的模型可以描述平均放电率和三磷酸腺苷 (ATP) 成本之间的关系，并在诸如肌萎缩侧索硬化症 (ALS) 等临床环境下复制神经元的行为。此外，EDLIF 模型在预测真正的尖峰列车方面表现出更好的性能——从某种意义上说尖峰巧合测量 - 比经典的泄漏积分和发射（LIF）模型。这里介绍的能量依赖模型的简单性使其计算效率高，因此适用于研究大型神经网络的动力学。